User-context-based search engine

ABSTRACT

A method and apparatus for determining contexts of information analyzed. Contexts may be determined for words, expressions, and other combinations of words in bodies of knowledge such as encyclopedias. Analysis of use provides a division of the universe of communication or information into domains, and selects words or expressions unique to those domains of subject matter as an aid in classifying information. A vocabulary list is created with a macro-context (context vector) for each, dependent upon the number of occurrences of unique terms from a domain, over each of the domains. This system may be used to find information or classify information by subsequent inputs of text, in calculation of macro-contexts, with ultimate determination of lists of micro-contests including terms closely aligned with the subject matter.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.11/500,607 filed Aug. 8, 2006, which claims the benefit of U.S.Provisional Patent Application No. 60/706,261, filed Aug. 8, 2005 andU.S. Provisional Patent Application No. 60/706,262, filed Aug. 8, 2005,all of which are hereby incorporated by reference herein in theirentities.

BACKGROUND

1. The Field of the Invention

This invention relates to a data extraction tool and, more particularly,to novel systems and methods for searching, organizing, and presentinginformation stored in electronic format.

2. The Background Art

In what is known as the information age, information is readilyavailable electronically, through information repositories known asdatastores and databases. Datastores are substantially unorganizedcollections of data, while databases are indexed in some fashion. TheInternet, the world's largest database, has made available enormousquantities of information to anyone with a personal computer andInternet access. This can be very helpful for people who wish to learnabout something or conduct business in the convenience of their ownhomes. However, it can also be tremendously time-consuming to locate adesired bundle of information among the millions available.

The Internet is organized only by the name of each web site. Eachindividual or group maintaining a web site decides how that web sitewill be organized. Thus, there is no official catalog of informationavailable on the Internet. Anyone desiring information must hypothesizewhich web sites would be likely to have the desired data and navigatethrough those web sites according to the organization set up by the website's operator. Although other databases and datastores are small, manyexhibit the same organizational difficulties.

Some companies have developed portals to automate a portion of thesearch for information. Most of these portals are text-based. Currentlyavailable portals include search engines, and directories.

To use a search engine, a user provides a set of words to search for,and the search engine returns a list of “hits” or web sites containingthose words. Search engines are advantageous in that they require littleuser input or understanding of the operation of the search engine.However, they can be difficult to work with for a number of reasons.

For example, the list may contain a vast number of hits, few of whichactually relate to the desired piece of data. Conventional keywordsearching returns any instance of the word being sought, regardless ofthe way the word is used in the web site. Although a user may addadditional key words to narrow the search, there often is no combinationof words that must be found together to exclude all irrelevant pageswhile keeping all relevant ones.

Also, many conventional search engines return only the home page of aweb site that contains the keyword. It is then up to the user to findthe keyword in a site and determine whether it is relevant. Thisrequires a user to figure out how the site is organized and follow theright links. This can be difficult because there may be no links thatclearly indicate where the keyword is.

The output from most search engines is simply a page of links topossibly relevant sites. A user may wish to supplement or rearrange thesearch results, but the way the results of a search are formattedtypically makes addition or modification of criteria difficult orimpossible.

Moreover, information obtained through a search often becomes outdated.Currently, a user must revisit previously found sites to determinewhether the old information is still valid. Additionally, a user mustperform a new search to locate any newly relevant sites and searchthrough those sites for relevant information.

Directories function differently than search engines. Rather than searchbased on keywords provided by a user, most directories provide a userwith an information scheme, often hierarchically organized. The userthen chooses what type of information to search for, designatingnarrower groups of information with each choice. Ultimately, the userreaches the bottom level of the hierarchy and receives a list of linksto information within that level.

Directories are advantageous in that information concerning a certaintopic is typically grouped together. A directory probably will notinundate a user with information, but rather provide a few linksbelieved to be important by the creators of the directory. Nevertheless,directories have drawbacks of their own.

For example, traditional directories contain information deemed of valueby those who compile them. A user may have an entirely different view ofwhat is important and what is irrelevant. A user may thus find thatinformation he or she needs simply is not available on the directory.

Also, directories take time to navigate. A user must make a series ofdecisions to reach any useful information at all. Even then, a user mayfind it necessary to backtrack and choose a different route through thehierarchy. Since a user cannot fashion groupings of information, he orshe may be required to view several branches of the hierarchy to obtainthe full range of information he or she desires.

Moreover, if a user does not know how to classify the bit of informationsought, he or she may not even he able to find it in the directory. Forexample, a user desiring to find the meaning of “salmonella” in abiological directory may spend great amounts of time looking through the“aquatic life” branch of the directory, without ever realizing that“salmonella” is more properly classified as “microscopic life.” The morea user's view of how information should be organized differs from thatof the directory's creators, the more difficult it will be for the userto find information in the directory.

Consequently, there is a need for a data extraction tool capable ofproviding many of the benefits of both search engines and directories,without drawbacks listed above. For example, there is a need for a toolthat could reliably provide a list of highly relevant informationlocations based on a simple text query. Furthermore, such a tool shouldprovide ready access to the exact location of the information.Preferably, the tool would supply the user with a list of locations orlinks that can be easily sorted and updated for the convenience of theuser. Furthermore, the tool should not require that the user understandthe configuration of the tool's internal databases.

In addition to the problems mentioned above, current searching methodsare deficient in a number of other ways. Consequently, a more advanceddata extraction tool may provide numerous benefits to those desiring toobtain information from a large datastore or database, such as theInternet.

BRIEF SUMMARY OF THE INVENTION

In view of the foregoing, in accordance with the invention as embodiedand broadly described herein, a method and apparatus are disclosed inone embodiment of the present invention for determining contexts ofinformation analyzed. Contexts may be determined for words, expressions,and other combinations of words in bodies of knowledge such asencyclopedias. Analysis of use provides a division of the universe ofcommunication or information into domains, and selects words orexpressions unique to those domains of subject matter as an aid inclassifying information. A vocabulary list is created with amacro-context (context vector) for each, dependent upon the number ofoccurrences of unique terms from a domain, over each of the domains.This system may be used to find information or classify information bysubsequent inputs of text, in calculation of macro-contexts, withultimate determination of lists of micro-contests including termsclosely aligned with the subject matter.

In one embodiment, databases of information may be mined to determinethe macro-contexts and micro-contexts for any constituent size ofinformation element. For example, web pages, groups of web pages,classification trees of web page content, or the like may be mined andanalyzed to determine macro-context and micro-context appropriate toeach. Thereafter, the database information may be indexed in accordancewith the macro and micro-context.

Upon receiving a query from user, the query may be given contexts, bothmacro and micro-contexts based on the query alone, the query associatedwith other queries from the same user, the query associated with otherinformation or query results from that user, or other inputs providedto, from, or about the user in order to give context. In one embodiment,the user may actually input documents and ask or request that the searchengine find documents like it in subject matter. Ultimately, upondetermining the macro and micro-context associated with the query, asearch engine may locate in a database information having contexts thatrelate most closely with the contexts associated with the query.Presentation to a user may be in any format suitable for understandingby a user.

The disclosure of U.S. patent application Ser. No. 09/630,753, filedAug. 2, 2000 for USER-CONTEXT-BASED SEARCH ENGINE, is herebyincorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing features of the present invention will become more fullyapparent from the following description and appended claims, taken inconjunction with the accompanying drawings. Understanding that thesedrawings depict only typical embodiments of the invention and are,therefore, not to be considered limiting of its scope, the inventionwill be described with additional specificity and detail through use ofthe accompanying drawings in which:

FIG. 1 is a schematic block diagram illustrating a computer system forexecuting certain methods and processes in accordance with the presentinvention.

FIG. 2 is a schematic diagram of a universe of communication orinformation populated by various communication elements and domains ofsubject matter in accordance with the present invention;

FIG. 3 is a schematic block diagram illustrating a method fordetermining a macro-context for an expansive set of communicationelements in accordance with the present invention;

FIG. 4 is a schematic block diagram illustrating one embodiment of amacro-context matrix or vector in accordance with the present invention;

FIG. 5 is a schematic block diagram illustrating a method fordetermining the macro and micro-contexts of an input text in accordancewith the present invention; and

FIG. 6 is a schematic block diagram illustrating a method forcontextually searching a database.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

It will be readily understood that the components of the presentinvention, as generally described and illustrated in the drawingsherein, could be arranged and designed in a wide variety of differentconfigurations. Thus, the following more detailed description of theembodiments of the system and method of the present invention, asrepresented in the drawings, is not intended to limit the scope of theinvention, as claimed, but is merely representative of variousembodiments of the invention. The illustrated embodiments of theinvention will be best understood by reference to the drawings, whereinlike parts are designated by like numerals throughout.

Referring to FIG. 1, an apparatus 10 or system 10 for implementing thepresent invention may include one or more nodes 12 (e.g., client 12,computer 12). Such nodes 12 may contain a processor 14 or CPU 14. TheCPU 14 may be operably connected to a memory device 16. A memory device16 may include one or more devices such as a hard drive 18 or othernon-volatile storage device 18, a read-only memory 20 (ROM 20), and arandom access (and usually volatile) memory 22 (RAM 22 or operationalmemory 22). Such components 14, 16, 18, 20, 22 may exist in a singlenode 12 or may exist in multiple nodes 12 remote from one another.

In selected embodiments, the apparatus 10 may include an input device 24for receiving inputs from a user or from another device. Input devices24 may include one or more physical embodiments. For example, a keyboard26 may be used for interaction with the user, as may a mouse 28 orstylus pad 30. A touch screen 32, a telephone 34, or simply atelecommunications line 34, may be used for communication with otherdevices, with a user, or the like. Similarly, a scanner 36 may be usedto receive graphical inputs, which may or may not be translated to otherformats. A hard drive 38 or other memory device 38 may be used as aninput device whether resident within the particular node 12 or someother node 12 connected by a network 40. In selected embodiments, anetwork card 42 (interface card) or port 44 may be provided within anode 12 to facilitate communication through such a network 40.

In certain embodiments, an output device 46 may be provided within anode 12, or accessible within the apparatus 10. Output devices 46 mayinclude one or more physical hardware units. For example, in general, aport 44 may be used to accept inputs into and send outputs from the node12. Nevertheless, a monitor 48 may provide outputs to a user forfeedback during a process, or for assisting two-way communicationbetween the processor 14 and a user. A printer 50, a hard drive 52, orother device may be used for outputting information as output devices46.

Internally, a bus 54, or plurality of buses 54, may operablyinterconnect the processor 14, memory devices 16, input devices 24,output devices 46, network card 42, and port 44. The bus 54 may bethought of as a data carrier. As such, the bus 54 may be embodied innumerous configurations. Wire, fiber optic line, wirelesselectromagnetic communications by visible light, infrared, and radiofrequencies may likewise be implemented as appropriate for the bus 54and the network 40.

In general, a network 40 to which a node 12 connects may, in turn, beconnected through a router 56 to another network 58. In general, nodes12 may be on the same network 40, adjoining networks (i.e., network 40and neighboring network 58), or may be separated by multiple routers 56and multiple networks as individual nodes 12 on an internetwork. Theindividual nodes 12 may have various communication capabilities. Incertain embodiments, a minimum of logical capability may be available inany node 12. For example, each node 12 may contain a processor 14 withmore or less of the other components described hereinabove.

A network 40 may include one or more servers 60. Servers 60 may be usedto manage, store, communicate, transfer, access, update, and the like,any practical number of files, databases, or the like for other nodes 12on a network 40. Typically, a server 60 may be accessed by all nodes 12on a network 40. Nevertheless, other special functions, includingcommunications, applications, directory services, and the like, may beimplemented by an individual server 60 or multiple servers 60.

In general, a node 12 may need to communicate over a network 40 with aserver 60, a router 56, or other nodes 12. Similarly, a node 12 may needto communicate over another neighboring network 58 in an internetworkconnection with some remote node 12. Likewise, individual components mayneed to communicate data with one another. A communication link mayexist, in general, between any pair of devices.

Referring to FIG. 2, a universe 62 of communications exists. Theuniverse 62 of communications has at an atomic level individual words.Words may themselves be terms of art or may be terms having independentmeaning. Likewise, words may be combined in expressions that havemeaning. Similarly, words may also be combined simply as words to have ameaning constituting a term.

For example, car is a word. Vehicle is a word. Truck is a word. Each ofthese has meaning. Nevertheless, a car may be an automobile driven by aindividual. Likewise, a car may be a railroad car operated by only arailroad. Thus, different words have different contexts which give theindividual words meaning.

Likewise, terms may be made from combined words. For example, a Fordcar, a touring car, or the like may be a particular type of a car andmay have individualized meaning because of the fact that the addition ofanother word creates yet another term and meaning.

Likewise, when considering expressions, many terms of art, and literaryexpressions may arise. For example, the expression “last minute” doesnot literally mean the last minute of anything. It is a figurativeexpression meaning late in some allocated period of time.

Thus, in general words, terms, expressions, and the like are part of thepopulation of communication elements 64 in the universe 62 ofcommunication. Of course, the universe 62 may expand into phrases, suchas clauses of individual sentences or phrases that are longer strings ofwords having some type of meaning. Likewise, individual sentences may bethought of as a group of assertions or declarations, in which a verbconstitutes or represents some type of activity or action, andassociated therewith is a nominative actor responsible for the action,and possibly an objective term that represents some thing or personacted upon, or some property or characteristic of the actor tied theretoby the verb.

Above assertions may be entire quotations, or larger textual groups ofwords. In general, any element of the universe 62 of communications maytake on, through a common usage recognized by some group, a meaning thatcan be recognized, documented, and used in searching, determiningcontext, understanding intended meaning, and the like.

In general, communication elements 64, whether they be from the smallest(atomic) level, or the greatest (macroscopic) level, may exist and doexist in the communication universe 62. Whenever an individual ordocument uses a particular communication element 64, it is used in acontext that will influence how that communication element 64 (e.g.,word, term, expression, phrase, assertion, etc.) is to be interpreted.

Interestingly, some words in the English language, and other languages,pertain to many different areas of subject matter. Thus, one may thinkof the universe 62 of communication as containing numerous domains 66 ofsubject matter. For example, the various domains 66 in FIG. 2 refer tocenters of meaning or subject matter areas. These domains 66 arerepresented as somewhat indistinct clouds, in that they may accumulate avocabulary of communication elements 64 about them that pertain to themor that relate to them. Nevertheless, some of those same communicationelements 64 may also have application elsewhere. For example, a horse toa rancher is an animal. A horse to a carpenter is an implement of work.A horse to a gymnast is an implement on which to perform certainexercises. Thus, the communication element 64 that we call “horse”belongs to, or pertains to, multiple domains 66.

In a method and apparatus in accordance with the invention, searchingfor a suitable response to a query submitted by a user to any searchengine may be improved substantially over a simple matching of words inthe body of information search. That is, in traversing the universe 62of communications, searching by individual word elements within thecommunication element 64 reduces a search to the most basic atomicelement 64 that can be assembled into communication. Thus, an individualword such as “the” is likely useless in a search. The word is soubiquitous and adds so little meaning to an expression, that to searchfor it is to obtain substantially no useful output. Thus, other words orcombinations of words that have more significance are more important forsearching. Search engines, however, that simply search for groups ofwords and their existence are still comparatively clumsy and providenumerous results that are not related to the subject matter domain 66that a particular user seeks. Typical search engines, require a user tocontinually refine the words used in a query, regardless of the actualsubject matter context that the user is pursuing.

In an apparatus and method in accordance with the invention, a searchengine process is developed that provides a deterministic method forestablishing context for the communication elements 64 submitted in aquery. Thus, it is possible for a search engine now to determine towhich domain 66 or domains 66 a communication element 64 is “attracted.”Since few things are absolute, domains 66 may actually overlap or bevery close such that they man share certain communication elements 64.That is, communication elements 64 do not “belong” to any domain 66,they are attracted to or have an affinity for various domains 66, andmay have differing degrees of affinity for differing domains 66. One maythink of this affinity as perhaps a goodness of fit or a best alignmentor quality alignment with the subject matter of a particular domain 66.

Referring to FIG. 3, context may be provided for various communicationelements 64 in association with various domains 66 by an engineimplementing a process 70. Such a process 70 may be performed by or incombination with an apparatus 10.

For example, one may divide 72 a universe 62 or a portion of a universe62 of communication into various domains 66. Domains 66 may be selectedby any suitable mechanism. Certainly every word or every individualcommunication element 64 is not its own domain. Likewise, dividing theentire universe 62 of information and communication into things plant,things animal, and things inanimate, may not be particular useful. Thus,somewhere between permitting every communication element 64 to be itsown domain, and dividing the entire universe 62 of information andcommunication into three domains, one may select a degree of granularitythat will be both computationally effective for searching, andcontextually rewarding. In one embodiment of an apparatus and method inaccordance with the invention, it has been found that a number ofdomains 66 on the order of about 100 or more has been foundcomputationally effective and contextually rewarding for comparativelyrapidly for searching.

By contextually rewarding is meant that the information to which theuser is directed, or the information provided back in response to aquery is comparatively closely related. Most contextually rewardingwould be that information exactly which has been sought. Nevertheless, alittle less contextually rewarding, but still useful would beinformation that is in the area and can still be useful. Contextuallyunrewarding would be information that shares the word in a completelydifferent and useless context related to the query. The subject matterof domain 66 may include things like medicine, geographic regions of theworld, sports, education, common activities, and the like.

Thus, once the universe 62 has been divided into some set of domains 66,one may create 74 domain lists of selected terms unique to each domain66. That is, it has been found that although numerous words and othercommunication elements 64 serve double or other multiple duty forvarious domains 66, each domain 66 may have certain communicationelements 64 that are substantially unique to itself. For example, inmedicine, oncology is a field relating to cancer. Such a term does nothave ubiquitous use in any field outside medicine. However, within thedomain 66 of medicine, the word is used frequently.

This is another useful characteristic, frequency. That is, acommunication element 64 such as an individual word or expression thatis used frequently, and exclusively within a particular field or subjectmatter domain 66, is particularly well suited for selection as one ofthe domain list terms.

Typically, a domain list of about 40 to 50 terms have been found to beeffective. Some domain lists have been operated successfully in anapparatus and method in accordance with the invention with as few as 10terms. Some domain lists may contain a few hundreds of individual terms.For example, some domains 66 may justify about 300 terms. Although themethod 70 is deterministic, rather than statistical, it is helpful tohave about 40 to 50 terms in the domain list in order to improve theefficiency of the calculations and determinations of the method.

The domain lists have utility in quickly identifying the particulardomain 66 to which their members pertain. This results from the lack ofcommonality of the terms and the lack of ambiguity as to domains 66 towhich they may have utility. By the same token, a list as small as thedomain lists are necessarily limited when considering the overallvocabulary of communication elements 64 available in any language. Thus,the terms in domain lists do not necessarily arise with the frequencythat is most useful for rapid searching. That is, a word that is uniqueto a particular subject matter domain 66, but infrequently used, may notarise in very many queries submitted to a search engine.

A process 76 for creating a vocabulary list of a substantial universe 62or a substantial portion of a universe 62 of communication elements 64may be performed by identifying 78 a body or corpus of informationorganized by topical entries. Thereafter, the text of each of thoseentries identified 78 may be subjected to a counting process 80 in whichoccurrences of terms from the domain list occur within each of thetopical entries. Ultimately, a calculation 82 of a macro context may bemade for each of the topical entries. This calculation is based on thedomain lists, and the domains represented thereby.

To identify a body or corpus of information, one may look to variousreference books. For example, an encyclopedia of computer terms mayprovide a vocabulary for people dealing in that particular portion ofthe universe 62. Likewise, a general encyclopedia of public knowledgemay be considered a corpus. Similarly, a dictionary has a certain aspectof organization in a regular format by toxical entries. Nevertheless, adictionary may be somewhat antithetical to the concept of body ofknowledge in that the entries are typically very short, and many andeach is a different context.

For processing, an apparatus 10 (e.g., computer 10, computer network 10)may look for words. Accordingly, the corpus of information should be putin some type of a digital format. For example, a digital copy of adocument, a digital on-line encyclopedia, a digital reference book, orthe like may form a corpus of interest.

Counting 80 may be done by an apparatus 10 simply searching for termsfound in the domain lists of the domains 66 into which the universe 62has been divided. The terms may be individual words, terms, expressions,phrases, and so forth. Likewise, an individual word may be reviewed andcounted as a word, and also counted in a expression. Similarly, in anassertion such as a subject, verb, and object, each individual word maybe counted as a word. Likewise, the assertion, if it has formed a termfound in a domain list, may also be treated in its entirety as anelement 64 to be counted.

Counting, in one embodiment of an apparatus and method in accordancewith the invention may involve selecting the topical entry, and for thatentry, identified by the title, or expression being described, countingthe occurrence of every term from every domain list available. Once thecount has been made for an entry, a calculation 82 of the macro contextfor that entry may be made.

One may think of a topical entry as a vocabulary term. That is, everytopical entry is a vocabulary word, expression, place, person, etc. thatwill be added to the overall vocabulary. That is, for example, theuniverse may be divided into about 100 to 120 domains 66 for convenientnavigation. Likewise, the domain lists may themselves contain from about10 to about 300 select terms each. By contrast, the topical entries thatmay be included in the build 76 of a vocabulary list may include thenumber of terms one would find in a dictionary such as 300 to 800,000.Less may be used, and conceivably more. Nevertheless, unabridgeddictionaries and encyclopedias typically have on this order of numbersof entries

Referring to FIG. 4, a macro-context 90 may be represented as a vector90. One may also think of the macro-context 90 as a set of orderedpairs. That is, for a first category 92, every individual domain 66identified may be listed. Thus, the individual entries 94 correspond tothe different domains 66 into which the universe 62 has been subdivided.The second category 96 is an occurrence or a weight 96. Thus, eachindividual entry 98 represents a waiting for a particular domain entry94. The resulting matrix is one example of how to create a macro-context90. In this embodiment, in accordance with the invention, all of thedomains 66 are represented by numbers 1, 2, 3, 4, 5, etc. in the entries94. The weighting entries 98 represent the number of occurrences of thedomain list words or terms created in the creation step 74. As apractical matter, the total number of occurrences of all domain listterms from a particular domain 66 are represented in a single entry 98.Actually, every single domain list term could be listed in a matrix ofweights 96 in the macro-context 90. However, this adds much additionalcomputation later, and actually can inhibit rapid searching. That is, bynarrowing the granularity to a too-fine level of scrutiny, searching maybe less likely to find or match a particular domain 66. Moreover,computations go up geometrically with the number of elements that mustbe processed. In the simplified approach illustrated in FIG. 4, themacro-context 90 may be readily calculated by a series of simpleweighting entries 98 corresponding to respective domain identifiers 94.

Moreover, the macro-context 90 may be further reduced by limiting theparticular entries 94 or domain identifiers 94 to the top 10 or 20weight values 98. Accordingly, zeros would disappear, and the highestnumbers in the entries 98 would be selected. In one embodiment, thegreatest ten weights 98 and their associated domain numbers 94 are laidinto a vector or matrix. If one thinks of all the domain numbers 94,then the weights 98 may be a vector, in that all the domain numbers areidentical, and complete, and the matrix is sparsely populated by the topten values of weights 98, and all other weights are set to zero. Thatis, a macro-context 90 may be thought of as a weight vector 96 includingthe weight for every available domain number 94. Meanwhile, the weightvector 96 is a sparse matrix or sparse vector in which all values areset to zero except the greatest ten or other number of entries 98.

In the example of FIG. 4, the domain 66 associated with the domainidentifier “1” has no term from its domain list occurring in the topicalentry identified in the macro-context 90. The domain 66 identified bydomain identifier “2” has 24 total occurrences of domain list of terms.This number of 24 may represent a single term multiple times, 24separate terms from the domain list each occurring once, or anycombination there between. Thus, each occurrence of each term may becounted as a weight value of 1. Other weighing schemes may be used inaccordance with mathematical theories available for numerical methods.For example, each term could be given away. Nevertheless, suchcomplexities have not been found to be necessary, and computationalspeeds are improved by simplicity.

For each topical entry or vocabulary term, a macro-context vector 90 maybe created. Thereafter, the unique vector for that entry may be used byapplying it to various inputs provided by searchers. In general, aninput may be from any source. Typically, an input may result from theterms of a query. Nevertheless, an input may also be not only the termsor words used in a query, but an extensive discourse or freeformparagraph describing what a person is searching for. Likewise, inputsmay include all previous text inputs from a particular user.

Likewise, an input may include information to be characterized orclassified. That is, a query from a searcher is classified in order toselect information qualifying to respond to the query. By the sametoken, additional resources on the Internet to be classified asresponses to be delivered may also be classified.

Referring to FIG. 5, a process 100 for determining context of an inputmay include providing 102 some type of input. Typically, the input willbe provided 102 as text. That is, a digital string of characters thatmay be processed. Typically, since human beings communicate in language,information is typically predominantly text corresponding to words, withinterspersed numbers and the like. Thus, providing an input text 102 mayinclude providing the body of a web page to be characterized, providinga query from a user to be responded to, providing the entire queryhistory of a user in order to provide some indication of the propercontext for responding to a current query from a user, or the like.Likewise, a user could be characterized by not only the previous queriessubmitted by that user, but by the responses to previous queries of thatuser.

In one embodiment of an apparatus and method in accordance of theinvention, a user may browse the Internet. Browsed pages may be provided102 for evaluation, along with the queries that gave rise to those.Thus, one may begin to obtain information to either classify informationthat will eventually be searched (e.g., providing 102 input textcorresponding to something published on the internet, or may be provided102 from inputs associated with a searcher.

An identification step 104 may create a vocabulary list of terms fromthe input text. That is, for example, the input text provided 102 may beparsed to find individual communication elements 64 such as words,terms, expressions, phrases, and the like that occur within the inputsprovided 102. In order to recognize that a communication element 64 is arecognized term of some type, that communication element 64 must existin some recognizable form. That is, a string matching may be done.However, in an apparatus and method in accordance with the invention,one method for providing useful identification of terms is to comparethe various communication element 64 with the vocabulary list that wasprovided during the build process 76. Thus, all the occurrences of anycommunication element 64 within the list resulted from the vocabularybuild 76 may be identified for its number occurrences within the inputtext.

Once the individual terms have been identified, then a calculation 106may calculate the macro-context for the input-text. In contrast to thebuilding process 76, in which the macro-context 90 was created for eachvocabulary entry, the calculation step sums the domain weights 98 fromthe macro-context vector 90 of each of the terms from the input text,according to those terms macro-contexts 90 in the original vocabularylist. Accordingly, in one embodiment, the macro-context of an input maybe a composite superposition or addition of the weights 98 of themacro-context vector 90 for all of the terms from the vocabulary listfound in the input text.

Another way to think of the calculation step 106 is that one may takeall the macro-context vectors 90, and particularly the weight vector 96for each term out of the vocabulary list from the build 76 that has beenfound within the input text provided 102. These vectors may be thoughtof as sparse vectors 96 in which some of the weights are zero and somehave values. As discussed above, for example, the vectors 96corresponding to the various vocabulary terms from the build 76 thatappear in the text provided 102 may be lined up and have the top tenweights 98 valued and all other weights set to zero. Accordingly, oncelined up, all the vectors may be added to provide a new vector with allof the summed values for all of the weighting entries. Thus, themacro-context vector 90 corresponding to the input text provided 102 isthis new summation vector. Thus, one may determine or calculate 106 amacro-context 90 corresponding to the input text provided 102.

In some instances, various strings of characters may exist in an inputtext provided 102 that do not match any vocabulary entry from the build76. According, these will not affect the calculation. Such terms mayinclude, for example, long strings of numbers. Likewise, foreign wordsnot in common usage may be included. Likewise, various grammaticalconstruction terms such a definite and indefinite articles may befiltered out or not included in the vocabulary, as being words that aretoo ubiquitous to be significant in any domain.

The process 100 may then use a deterministic mathematical process fordetermining a micro-context. A micro-context may be a list of termsselected from the vocabulary list created in the build 76. The list ofterms is constructed by determining 108 the entries from the vocabularylist in the build 76 that are most closely aligned with the input textprovided 102. This may be done in one of several ways. For example, theweight vector 96 vor any term of the vocabulary list may be multiplied(e.g., as a dot product) by the macro-context 90 of the input text. Thismay be done for each of the vocabulary entries found in both the inputtext and vocabulary list.

Various numerical methods may be used for comparison. One very simplemethod is to simply multiply the macro-context vector for the input textprovided 102 against that of each of the terms in the vocabulary listthat are found therein. If desired, this may be normalized by dividingby the multiplication of the weight vector 96 or macro-context 90 of thevocabulary entry against itself. Other types of fit routines, comparisonroutines, correlation routines and the like may be used in order todetermine which vocabulary entries from the build 76 provide the highestvalues of the dot product from multiplication value of the two vectors.Accordingly, those vocabulary entries that have the highest values maybe provided in a list. In one embodiment, determining a micro-contextlist involves selecting the best aligned or highest valued correlationsto pick the most closely aligned vocabulary entries for the list of thedetermination step 108. Typically, it has been found in one embodimentof an apparatus and method in accordance with the invention that 256most closely aligned vocabulary entries, identified with associatedweightings, form a suitable micro-context for the input text provided102.

Referring to FIG. 6, a process 110 or method 110 for searching adatabase of information may include mining 112 a database or otherrepository of information to determine macro and micro contexts forconstituent elements thereof. For example, any constituent element of adatabase or information repository may simply be an identifiable portionthereof. In a database, a constituent may be a record. In a record, aconstituent may be a particular field of text. Nevertheless, in adatabase a record may be a particular level. For example, the Internetcontains a great amount of information simply published on individualweb pages. Thus, a web page, a web site, or a group of web pages groupedon a web site under a particular heading may all be considered aparticular input text to be provided 102 as a recognizable entity. Thus,for example, an apparatus and method in accordance with the inventionmay characterize a web page, a group of web pages within a particularsub-category on that web site, or the entire web site in accordance withthe processes disclosed herein above.

Following mining 112 of information, indexing 114 may involve sorting,classifying, filtering, or otherwise organizing information according tovalues of the macro-contexts thereof, or according to the word lists orterms lists found in micro-contexts. For example, content mined 112 maybe organized according to all the micro-contexted terms that have thehighest values. For example, the highest 10, 20 or 256 terms used mayall be tags under which such a content would be indexed. Likewise,content from a location that has been mined 112 may be indexed 114according to the shape and values of entries 98 in the macro-contextvector 96.

Upon receiving 116 a query from user, the process 110 may determine 118,the macro and micro-context associated with the query. The query itselfmay be used as an input text 102 as described above in order todetermine these contexts. Alternatively, all the queries submittedpreviously by that particularly user may also be included as input textprovided 102 in order to determine 118 these contexts. Also, asdescribed herein above, browsing results or search results previouslyprovided in a response to queries from the user may all be used as partof the input text provided 102 in order to associate with the query someappropriate context. In one embodiment, a user may even donate to asearch engine other material for which the user desires likeinformation. This may include articles, web pages, or other informationthat the user has in digital format for submission to the process 110 inorder to indicate that more of this type of material is desired.

Locating 120 in a database of information entries or information thathas a context sufficiently related to the context associated with thequery may be done by any simple method. For example, macro andmicro-contexts for a query may be compared and matched. Once located,such information may be presented 122 to a user.

Example Embodiment I

This embodiment of the invention relates to the field of on-lineadvertising, especially as it relates to personalization and senseanalysis for advertisers' web sites. A big problem with thepay-per-click models used in online advertising is that they can bespoofed by people who are interested in costing online advertisers moneyinstead of finding out more about their products. The present inventionprovides a system to certify that the user who is clicking though on thead or link actually has a history of looking at similar domains in thepast. With this history, or personalization profile, the userinteraction rises to the level of “certified click.”

Every online advertisement has a destination URL. Each of these URLs canbe semantically classified using a numerical vector that specifiesmembership in each of some number N of domains. For this invention, adomain can be defined as a partition of some semantic space of interest,where the partition is defined using an enumeration of semantic entitiesthat occur exclusively or primarily in that part of the overall space. AURL can be considered to belong strongly to that domain if it contains asignificant number of the semantic entities that make up that domain.Conversely, a URL would be considered to belong weakly or not at all toa domain if it contained few or none of the semantic entities used todefine that domain.

Similarly, a person can be assigned a membership vector to the same setof domains simply by examining their search and click history. Sinceevery URL or even entire web site can be assigned a vector, assigning aperson a similar vector is accomplished by simply combining the vectorsfrom the sites they visit in some way. This combination could occur inmany different ways, including (but not limited to) averaging, averagingwith hysteresis, weighted combination, or noise-adding averaging. Thisvector created for a person is called a “personalization vector.”

With a personalization vector and a vector for every web site. Theuser's personalization vector and a site's vector can be multipliedtogether using a dot product, a weighted dot product, or some othermethod, resulting in a match value M. The advertiser can select amatching threshold T, where if a user coming into the target site has anM that exceeds the T, a certified click is generated. This certifiedclick may motivate greatly increased ad rates for the hosting site, ormay be used to measure other things relating to marketing effectiveness.

Systems in common use today capture only the number of clicks made on anad link, or at best the number and source of those clicks. The back endof this system measures all this as well as the quality of each click.Using simple tagging measures commonly utilized today, a correlation canbe developed between the conversion rate at the target site and theclick quality from the tagged sources. Modifications to the matchingfunction can then be made using a procedure similar to one layer backpropagation in neural networks, allowing optimization for any set ofdomains. A side effect of this is that it is possible to measure theutility of each of the domains selected for the semantic space based onthe weights found for each domain after the propagation phase.

The present embodiment of the invention gives the user a new way ofmeasuring the quality of visitors to their websites, where quality is ameasure of the match between a visitor's demonstrated interests and thecontent of the web page. It relies on three constructs—partition of asemantic space into a set of domains, definition of those domains interms of semantic entities, and a mechanism to construct and maintain apicture of a given user with respect to one of more sets of domains.

Partitioning a semantic space into a set of domains is a manual process.To do this, a domain expert may delineate the semantic loci in thespace. Mathematically speaking, it does not matter what the domains arecalled or even what their relative size is in terms of a random trainingset being assigned as members—it only matters that a partition is madethat can be well-separated with a finite list of semantic entities, suchthat any semantic entity is a member of a very small fraction of thedomains and each domain has many such entities.

Semantic entities can be anything from simple keywords to semanticconstructs, which are assertions that are made up of simpler entitiestied together with rules. The only constraint on a semantic entity isthat it can be extracted from some sequence of combinations of raw text.In most cases, the set of semantic entities used to represent a domainis derived from a list of master assertions commonly answered by text inthe domain. For example, if the domain in question is Breast Cancer, onequestion that is commonly answered is what stage of cancer a patient isgenerally at. By compiling a list of domain-specific questions, it ispossible to (1) specify differences between very similar domains withgreat precision, and (2) create a rapid way to prototype a domain thatdoes not require many hours of an expert's time, and can be expanded byrelatively inexperienced people.

Where it is only necessary to classify a piece of text rather thanextract actual values for the assertions in the domain, the masterassertions can be “factored” into their simplified descendants and thosewhich are common to a large percentage of the domains in question canthen be removed, leaving (mostly) a list of keywords and phrases todefine a domain. This speeds the comparison and classification processestremendously.

The mechanism to create and maintain a domain-based picture (orsense-based picture) of a user can be as simple as a cookie stored onthe user's machine that is used to update a user vector stored in acentral database. This only functions when the user is surfing throughsites that participate in the personalization system. Another slightlymore complex implementation is something like a Browser Helper Object(BHO) that runs on the user's machine and watches/categorizes allsurfing activity. With this system, even non-participating sites cancontribute to the picture of the user, and any clicking the user does toad sites served by certified clicks will pick up a much morecomprehensive picture.

The system starts with zero knowledge about the user. When the userbegins browsing, either through a participating site or by initializingthe BHO, a new record is created in the database for that user. Everytime the user clicks on a link, the contents of the returned page areused to update the picture of the user in the manner summarized above.The user may control the image being built up, or may even createmultiple images of him or herself and select which image is presented asthe current valid representation of the user's state. These images, or“aspects,” represent the different roles that a given person might takeon during the course of time. For example, a person might have abusiness aspect, wherein they are looking for things like printers,software, and computers, and a vacation aspect, where they are searchingfor good tours through Chile and Argentina. Once aspects are created bythe user, browsing activity can be automatically assigned to the correctaspect simply by doing a global domain count match between the currentsite and all available aspects. Similarly, the user's current aspect orrole can be inferred from the sites being browsed.

Updating of aspects or core personalization attributes can happen eachtime a different site is visited, or on a more delayed basis (in thecase where the user is running the BHO). Historical data may bemaintained, however, it is not needed as the hysteresis maintains animplicit history.

Sites that implement a certified clicks program need only query thecentral database to resolve the identity of a user's aspect and verify asufficiently close match. The provided information can be additionallyused to customize the receiving site for the incoming user, based onancillary interests the user might have.

Example Embodiment II

This embodiment of the invention relates to the field of textprocessing, specifically to the fields of semantic analysis and textclassification. Semantic analysis of a block of text has the primarygoal of extracting meaning, or manipulatable assertions, from thecombination of words that make up the text. The extracted assertions maynot be explicitly present in the text, and may need to be inferred froma body of prior experience. One of the most important problems that mustbe solved before non-explicit assertions are made about the text is thatof domain identification. The present invention addresses that problem.

A domain can be defined as a set of base assertions that apply for agiven situation, for example, when we are talking about music, we canstate that notes, rests, harmony, and other music-related rules areimportant. We can talk about artists, songs, and genres. We can assumethat the rules and constraints of the music publishing industry areimportant. In short, before the first word of the text is processed, wecan state many facts with certainty and ask many questions, the answersto which may or may not be found in the text.

Solutions to the domain identification problem fall into severalcategories: Keyword-based solutions, mathematical or statisticalsolutions, and rule-based solutions are the major categories for thesesolutions.

Keyword solutions are probably the most commonly used. Domains arerepresented by lists of keywords or key phrases that are more likely tooccur in that particular domain than in any other domain. By explicitlyspecifying a domain through the use of manually selected keywords, arelatively good approximation to the domain may be achieved, unlessdiscrimination is being attempted between two different domains thatshare the same vocabulary (without regard to meaning).

Mathematical and statistical solutions rely on properties of large setsto provide an automatic discrimination function between domains ofinterest. These types of solutions require the user to pre-classifyblocks of text, often numbering in the thousands. These pre-classifiedblocks of text are then used as a training set for the system.Statistical calculations are run against all possible tokens andphrases, and those with the highest discrimination value are retainedfor use in classifying future inputs.

Rule-based solutions require manual construction of rules, which are runagainst the blocks of text to be classified. When enough rules aresatisfied in a given domain, the block of text is assigned to thatdomain. Assignment may occur to multiple domains, or to the domain thatsatisfies the most rules.

One big problem that all of the existing systems have is that domainssimply cannot be specified with sufficient precision to allowdiscrimination between two arbitrarily close domains. For example, ifusers require text domain segmentation between two types of computerprogramming message boards, where one deals with databases and the otherdeals with networks, both boards will use almost all of each other'skeywords and key phrases. Similarly, mathematical approaches will not beable to automatically find discrimination functions because of the lowgranularity of the domain space.

A second and more significant problem is the large degree of manualintervention required to create a single domain in many of the statedapproaches. Experts that know the domain are required to painstakinglyselect pertinent phrases in the keyword approaches, or manuallydiscriminate between every text fragment in the training sets in themathematical cases.

This embodiment of the invention gives the user a new way of specifyingdomains that is much more efficient than the existing methods. Asignificant effect of this new method of representation is the creationof a new way of representing the domain itself, as a set of slots to befilled, rather than a list of rules or keywords.

The domain representation system uses a list of assertions commonlyanswered by text in the domain to specify the domain itself. Forexample, if the domain in question is Breast Cancer, one question thatis commonly answered is what stage of cancer a patient is generally at.By compiling a list of domain-specific questions, it is possible to (1)specify differences between very similar domains with great precision,and (2) create a rapid way to prototype a domain that does not requiremany hours of an expert's time, and can be expanded by relativelyinexperienced people.

Verifying whether or not a block of text answers a specific question isa task for extractors. Extractors are a hierarchical rule-based systemin the form of a DAG whose leaf nodes consist of simple tokenidentifiers, whose middle level nodes consist of token combinationoperators, and whose top levels consist of assertion generators. Whentext is input to this system, it is broken up into a sequential list oftokens which activate the leaf nodes of the extractor system. Those leafnodes then activate higher level nodes using a neural network-likesystem of activation thresholds and mutual enhancement/suppressionlinks. That process continues until no further nodes are activated orsuppressed. The result is a representation of the text in assertionform, which can then be matched to the list of assertions previouslygenerated for each domain of interest. The domain with the most slotsfilled by the generated assertion is selected as the domain for thetext, and the unfilled slots in the domain list can be iterativelyfilled by pushing back down through the assertion graph and inferringvacant nodes from the master domain.

An important part of this embodiment of the invention is the processingof text through the extraction DAG and the matching of the generatedassertions with the core domain assertions. The user supplies aspecification of the domain using a piece of software that outputs thoseassertions in a normalized format. One such specification is whereassertions are specified as declarative sentences, and users designatethe name of the assertion and the value slot using the programmaticinterface. The assertion name is inserted into a database for use by theextractor builder.

The extractor builder is another piece of software that allows the userto select from the list of assertions for all domains and createrelationships between tokens that designate both the existence of theassertion in a block of text as well as the format and location of thevalue needed to complete the assertion. Such relationships may include,but are not limited to adjacency, font size, paragraph length, verb typecoexistence, voice, tense, keywords, key phrases, or numerical values.Users can also specify exceptions to these relationships, and usemodifications against existing relationships to create new relationshipsin the extractor structure.

Once a domain and an extractor structure are fully specified, the systemcan be run against arbitrary blocks of text. The text is stripped of allunneeded information and all possible nodes in the extractor structureare activated. The result is a list of assertions with associatedvalues. The domain with the most active assertions is reported as theselected domain. Those assertions which belong to the selected domainprovide their extracted values as the extracted semantic content forthat block of text. The next step is to infer additional semanticcontent to the block of text by force-activating all remainingassertions in the domain and looking for unused values at the tokenlevel that are not prohibited by extractor structure rules from fillingthose slots.

One aspect of this embodiment of the invention (among many) isextraction of factual and emotional content from on-line message boardsand blogs. The system can be programmed to seek out internet sourcesthat belong to a given domain, and then extract information of interestto that domain and insert the results in a conventional database. Theseresults can then be used to generate reports for marketing, brandidentification, or other purposes. They can also be used to measure theeffectiveness of ad campaigns or the effect of news stories aboutcertain products or events. Analysis by demographic can be accomplished,where demographic extractors are supplied with the domain.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrative,and not restrictive. The scope of the invention is, therefore, indicatedby the appended claims, rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

What is claimed and desired to be secured by United States LettersPatent is:
 1. A method for classifying information, the methodcomprising: providing input text; identifying a vocabulary listindependent from the input text, the vocabulary list comprising aplurality of entries, each of the plurality of entries associated with amacro-context, wherein the macro-context comprises a vectorcharacterizing the context of the entry by mapping a plurality ofsubject matters, each unique, to a corresponding plurality of weights,each weight reflecting a contribution of a corresponding subject matterof the plurality of subject matters to the entry; counting occurrencesof each term from the vocabulary list found within the input text;calculating a macro-context representing summations of themacro-contexts associated with the terms from the vocabulary list foundwithin the input text to characterize the context of the input text bymapping the plurality of subject matters to corresponding weightsreflecting contributions of corresponding subject matters of theplurality of subject matters to the input text; and determining amicro-context comprising a list of terms selected from the list ofvocabulary that correspond to the input text.
 2. The method of claim 1,further comprising reducing the list of terms in the micro-context byselecting those terms having macro-contexts most closely aligned withthe macro-context of the input text.
 3. The method of claim 1, whereindetermining a micro-context comprises identifying, from the plurality ofentries, entries having a macro-context within a selected mathematicalproximity to the calculated macro-context.
 4. The method of claim 1,wherein the plurality of entries comprises a plurality of topicalentries, each entry of the plurality of entries corresponding to atopical entry of the plurality of topical entries.
 5. The method ofclaim 1, wherein the macro-context of each of the plurality of entriesreflects the counted occurrences.
 6. The method of claim 1, whereindetermining a micro-context further comprises calculating amultiplication of at least two macro-context vectors to provide amathematical value reflecting a correspondence of a query with avocabulary list of topical entries.
 7. The method of claim 1, whereineach of the plurality of entries comprises at least one of a word, aname, or a phrase.
 8. The method of claim 1, providing input textcomprising at least one of providing a body of a web page to becharacterized, providing a query from a user, or providing a queryhistory of a user, the query history including previous queriessubmitted by the user and responses to the previous queries of the user.9. The method of claim 1, further comprising classifying the input textaccording to at least one of the macro-context or the micro-context. 10.A method for searching comprising: mining a repository of information todetermine macro and micro-contexts for elements of a database, eachmacro and micro-contexts characterizing the context of an element of thedatabase by mapping a plurality of subject matters, each unique, to acorresponding plurality of weights, each weight reflecting acontribution of a corresponding subject matter of the plurality ofsubject matters to the element of the database; indexing the databasecontent according to the macro and micro-contexts determined; receivinga query from a user; determining macro and micro-contexts associatedwith the query, the macro and micro-contexts characterizing the contextof the query by mapping the plurality of subject matters tocorresponding weights reflecting contributions of corresponding subjectmatters of the plurality of subject matters to the query; locating in adatabase information having contexts related to contexts associated witha query; and presenting the information located to a user.
 11. Themethod of claim 10, wherein the repository comprises the database. 12.The method of claim 10, wherein the macro-context is a vectorrepresenting weights corresponding to the use of vocabulary terms from alist of topical entries.
 13. The method of claim 10, wherein themicro-contexts represent a list of words most closely reflecting thecontent of the contexts of information in the repository.
 14. The methodof claim 10, wherein receiving a query from a user further comprisesreceiving additional information from a user selected from the groupconsisting of previous queries by the user, previous responses toprevious queries from a user, previous results from browsing by a user,and documents provided by a user to establish contexts.
 15. The methodof claim 10, wherein providing a micro-context further comprisescalculating a multiplication of at least two macro-context vectors toprovide a mathematical value reflecting a correspondence of a query witha vocabulary list of topical entries.
 16. The method of claim 10,wherein locating further comprises comparing a parameter reflecting atleast one context associated with the query to a corresponding parametercorresponding to a context of selected information from the repositoryof information.
 17. The method of claim 16, wherein the context comparedis selected from macro-context and micro-context.
 18. The method ofclaim 10, wherein the database comprises a plurality of fields of textassociated with at least one of a web page, a web site, or a group ofweb pages grouped on a web site under a heading.
 19. The method of claim10, wherein the repository of information comprises a plurality of termsand a plurality of topical entries, each term of the plurality of termscorresponding to a topical entry of the plurality of topical entries.20. The method of claim 10, wherein locating in a database informationcomprises comparing and matching the macro and micro-contexts of thedatabase with the macro and micro-contexts of the query.